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  <div class="headertitle"><div class="title">CSvmGridSearchTutorial.cpp</div></div>
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<a href="_c_svm_grid_search_tutorial_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="preprocessor">#include &lt;<a class="code" href="_gaussian_rbf_kernel_8h.html">shark/Models/Kernels/GaussianRbfKernel.h</a>&gt;</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="preprocessor">#include &lt;<a class="code" href="_zero_one_loss_8h.html">shark/ObjectiveFunctions/Loss/ZeroOneLoss.h</a>&gt;</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="preprocessor">#include &lt;<a class="code" href="_c_svm_trainer_8h.html">shark/Algorithms/Trainers/CSvmTrainer.h</a>&gt;</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="preprocessor">#include &lt;<a class="code" href="_data_distribution_8h.html">shark/Data/DataDistribution.h</a>&gt;</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span> </div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="preprocessor">#include &lt;<a class="code" href="_cross_validation_error_8h.html">shark/ObjectiveFunctions/CrossValidationError.h</a>&gt;</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="preprocessor">#include &lt;<a class="code" href="_grid_search_8h.html">shark/Algorithms/DirectSearch/GridSearch.h</a>&gt;</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="preprocessor">#include &lt;<a class="code" href="_jaakkola_heuristic_8h.html">shark/Algorithms/JaakkolaHeuristic.h</a>&gt;</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span> </div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="keyword">using namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a>;</div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="keyword">using namespace </span>std;</div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span> </div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span> </div>
<div class="foldopen" id="foldopen00014" data-start="{" data-end="}">
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno"><a class="line" href="_c_svm_grid_search_tutorial_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4">   14</a></span><span class="keywordtype">int</span> <a class="code hl_function" href="_c_svm_grid_search_tutorial_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4">main</a>()</div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span>{</div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>    <span class="comment">// problem definition</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>    <a class="code hl_class" href="classshark_1_1_chessboard.html" title="&quot;chess board&quot; problem for binary classification">Chessboard</a> prob;</div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span>    <a class="code hl_class" href="classshark_1_1_labeled_data.html">ClassificationDataset</a> dataTrain = prob.<a class="code hl_function" href="classshark_1_1_labeled_data_distribution.html#ace15c1b51c87cd4b553427a55416b155" title="Generates a dataset with samples from from the distribution.">generateDataset</a>(200);</div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>    <a class="code hl_class" href="classshark_1_1_labeled_data.html">ClassificationDataset</a> dataTest= prob.<a class="code hl_function" href="classshark_1_1_labeled_data_distribution.html#ace15c1b51c87cd4b553427a55416b155" title="Generates a dataset with samples from from the distribution.">generateDataset</a>(10000);</div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span> </div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>    <span class="comment">// SVM setup</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>    <a class="code hl_class" href="classshark_1_1_gaussian_rbf_kernel.html" title="Gaussian radial basis function kernel.">GaussianRbfKernel&lt;&gt;</a> kernel(0.5, <span class="keyword">true</span>); <span class="comment">//unconstrained?</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>    <a class="code hl_struct" href="structshark_1_1_kernel_classifier.html" title="Linear classifier in a kernel feature space.">KernelClassifier&lt;RealVector&gt;</a> svm;</div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span>    <span class="keywordtype">bool</span> offset = <span class="keyword">true</span>;</div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>    <span class="keywordtype">bool</span> unconstrained = <span class="keyword">true</span>;</div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>    <a class="code hl_class" href="classshark_1_1_c_svm_trainer.html" title="Training of C-SVMs for binary classification.">CSvmTrainer&lt;RealVector&gt;</a> trainer(&amp;kernel, 1.0, offset,unconstrained);</div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span> </div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>    <span class="comment">// cross-validation error</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>    <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> K = 5;  <span class="comment">// number of folds</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>    <a class="code hl_class" href="classshark_1_1_zero_one_loss.html" title="0-1-loss for classification.">ZeroOneLoss&lt;unsigned int&gt;</a> loss;</div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>    <a class="code hl_class" href="classshark_1_1_c_v_folds.html">CVFolds&lt;ClassificationDataset&gt;</a> folds = <a class="code hl_function" href="group__shark__globals.html#gabb5bec2fca9d1eaa2ea58c75d36d1195" title="Create a partition for cross validation.">createCVSameSizeBalanced</a>(dataTrain, K);</div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>    <a class="code hl_class" href="classshark_1_1_cross_validation_error.html" title="Cross-validation error for selection of hyper-parameters.">CrossValidationError&lt;KernelClassifier&lt;RealVector&gt;</a>, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&gt; cvError(</div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>        folds, &amp;trainer, &amp;svm, &amp;trainer, &amp;loss</div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>    );</div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span> </div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span> </div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>    <span class="comment">// find best parameters</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>    <span class="comment">// use Jaakkola&#39;s heuristic as a starting point for the grid-search</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span>    <a class="code hl_class" href="classshark_1_1_jaakkola_heuristic.html" title="Jaakkola&#39;s heuristic and related quantities for Gaussian kernel selection.">JaakkolaHeuristic</a> ja(dataTrain);</div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>    <span class="keywordtype">double</span> ljg = log(ja.<a class="code hl_function" href="classshark_1_1_jaakkola_heuristic.html#ae5e25ff2cd4a047ff31900f1f1aa3b55">gamma</a>());</div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>    cout &lt;&lt; <span class="stringliteral">&quot;Tommi Jaakkola says gamma = &quot;</span> &lt;&lt; ja.<a class="code hl_function" href="classshark_1_1_jaakkola_heuristic.html#ae5e25ff2cd4a047ff31900f1f1aa3b55">gamma</a>() &lt;&lt; <span class="stringliteral">&quot; and ln(gamma) = &quot;</span> &lt;&lt; ljg &lt;&lt; endl;</div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span> </div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>    <a class="code hl_class" href="classshark_1_1_grid_search.html" title="Optimize by trying out a grid of configurations.">GridSearch</a> grid;</div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span>    vector&lt;double&gt; min(2);</div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>    vector&lt;double&gt; max(2);</div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>    vector&lt;size_t&gt; sections(2);</div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>    <span class="comment">// kernel parameter gamma</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>    min[0] = ljg-4.; max[0] = ljg+4; sections[0] = 9;</div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>    <span class="comment">// regularization parameter C</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>    min[1] = 0.0; max[1] = 10.0; sections[1] = 11;</div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>    grid.<a class="code hl_function" href="classshark_1_1_grid_search.html#abb3472e263958e425bb45b3556d52e7c">configure</a>(min, max, sections);</div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>    grid.<a class="code hl_function" href="classshark_1_1_grid_search.html#afb6458a381e6b45cb8290289a573d642">step</a>(cvError);</div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span> </div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>    <span class="comment">// train model on the full dataset</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>    trainer.<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#aecde2bab6daf3fa44b94438c7ba79a24" title="set the vector of hyper-parameters">setParameterVector</a>(grid.<a class="code hl_function" href="classshark_1_1_abstract_single_objective_optimizer.html#a0909596fcc4f80a8d108859b20b64a81" title="returns the current solution of the optimizer">solution</a>().point);</div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>    trainer.<a class="code hl_function" href="classshark_1_1_c_svm_trainer.html#a9e801518bfba9d02e0749181a5deb0fc" title="Train the C-SVM.">train</a>(svm, dataTrain);</div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>    cout &lt;&lt; <span class="stringliteral">&quot;grid.solution().point &quot;</span> &lt;&lt; grid.<a class="code hl_function" href="classshark_1_1_abstract_single_objective_optimizer.html#a0909596fcc4f80a8d108859b20b64a81" title="returns the current solution of the optimizer">solution</a>().point &lt;&lt; endl;</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>    cout &lt;&lt; <span class="stringliteral">&quot;C =\t&quot;</span> &lt;&lt; trainer.<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>() &lt;&lt; endl;</div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>    cout &lt;&lt; <span class="stringliteral">&quot;gamma =\t&quot;</span> &lt;&lt; kernel.<a class="code hl_function" href="classshark_1_1_gaussian_rbf_kernel.html#aed0a3d8520b5c08499be99e860f2b2c8" title="Get the bandwidth parameter value.">gamma</a>() &lt;&lt; endl;</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span> </div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>    <span class="comment">// evaluate</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>    <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;unsigned int&gt;</a> output = svm(dataTrain.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>());</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>    <span class="keywordtype">double</span> train_error = loss.<a class="code hl_function" href="classshark_1_1_zero_one_loss.html#acba6670d53701d50eed0ecdbc1114175" title="Return zero if labels == predictions and one otherwise.">eval</a>(dataTrain.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>(), output);</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>    cout &lt;&lt; <span class="stringliteral">&quot;training error:\t&quot;</span> &lt;&lt; train_error &lt;&lt; endl;</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>    output = svm(dataTest.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>());</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>    <span class="keywordtype">double</span> test_error = loss.<a class="code hl_function" href="classshark_1_1_zero_one_loss.html#acba6670d53701d50eed0ecdbc1114175" title="Return zero if labels == predictions and one otherwise.">eval</a>(dataTest.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>(), output);</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>    cout &lt;&lt; <span class="stringliteral">&quot;test error: \t&quot;</span> &lt;&lt; test_error &lt;&lt; endl;</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>}</div>
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